

import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest







class     __lowercase (snake_case__					):
 _UpperCamelCase         =    (DDIMParallelScheduler,)
 _UpperCamelCase         =    (("""eta""", 0.0), ("""num_inference_steps""", 50))
 def       UpperCamelCase__    (				self ,				**A_   )   ->Any:



       '''simple docstring'''





       __lowerCAmelCase   :		Optional[Any]   =   {
           '''num_train_timesteps''': 1000,
           '''beta_start''': 0.0_001,
           '''beta_end''': 0.02,
           '''beta_schedule''': '''linear''',
           '''clip_sample''': True,
       }

       config.update(**__snake_case   )
       return config
 def       UpperCamelCase__    (				self ,				**A_   )   ->Union[str, Any]:



       '''simple docstring'''





       __lowerCAmelCase   :		List[Any]   =   self.scheduler_classes[0]
       __lowerCAmelCase   :		Any   =   self.get_scheduler_config(**__snake_case   )
       __lowerCAmelCase   :		Tuple   =   scheduler_class(**__snake_case   )

       __lowerCAmelCase,				__lowerCAmelCase   :		List[Any]   =   10, 0.0

       __lowerCAmelCase   :		Any   =   self.dummy_model()
       __lowerCAmelCase   :		Tuple   =   self.dummy_sample_deter

       scheduler.set_timesteps(__snake_case   )

       for t in scheduler.timesteps:
             __lowerCAmelCase   :		List[str]   =   model(__snake_case ,				__snake_case   )
             __lowerCAmelCase   :		str   =   scheduler.step(__snake_case ,				__snake_case ,				__snake_case ,				__snake_case   ).prev_sample

       return sample
 def       UpperCamelCase__    (				self   )   ->Any:



       '''simple docstring'''





       for timesteps in [100, 500, 1000]:
             self.check_over_configs(num_train_timesteps=__snake_case   )
 def       UpperCamelCase__    (				self   )   ->Dict:



       '''simple docstring'''





       for steps_offset in [0, 1]:
             self.check_over_configs(steps_offset=__snake_case   )

       __lowerCAmelCase   :		List[Any]   =   self.scheduler_classes[0]
       __lowerCAmelCase   :		int   =   self.get_scheduler_config(steps_offset=1   )
       __lowerCAmelCase   :		Optional[int]   =   scheduler_class(**__snake_case   )
       scheduler.set_timesteps(5   )
       assert torch.equal(scheduler.timesteps ,				torch.LongTensor([801, 601, 401, 201, 1]   )   )
 def       UpperCamelCase__    (				self   )   ->List[Any]:



       '''simple docstring'''





       for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1] ,				[0.002, 0.02, 0.2, 2]   ):
             self.check_over_configs(beta_start=__snake_case ,				beta_end=__snake_case   )
 def       UpperCamelCase__    (				self   )   ->List[Any]:



       '''simple docstring'''





       for schedule in ["linear", "squaredcos_cap_v2"]:
             self.check_over_configs(beta_schedule=__snake_case   )
 def       UpperCamelCase__    (				self   )   ->List[Any]:



       '''simple docstring'''





       for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=__snake_case   )
 def       UpperCamelCase__    (				self   )   ->Optional[int]:



       '''simple docstring'''





       for clip_sample in [True, False]:
             self.check_over_configs(clip_sample=__snake_case   )
 def       UpperCamelCase__    (				self   )   ->Optional[int]:



       '''simple docstring'''





       for timestep_spacing in ["trailing", "leading"]:
             self.check_over_configs(timestep_spacing=__snake_case   )
 def       UpperCamelCase__    (				self   )   ->int:



       '''simple docstring'''





       for rescale_betas_zero_snr in [True, False]:
             self.check_over_configs(rescale_betas_zero_snr=__snake_case   )
 def       UpperCamelCase__    (				self   )   ->List[str]:



       '''simple docstring'''





       self.check_over_configs(thresholding=__snake_case   )
       for threshold in [0.5, 1.0, 2.0]:
             for prediction_type in ["epsilon", "v_prediction"]:
                   self.check_over_configs(
                       thresholding=__snake_case ,				prediction_type=__snake_case ,				sample_max_value=__snake_case ,				)
 def       UpperCamelCase__    (				self   )   ->Optional[Any]:



       '''simple docstring'''





       for t in [1, 10, 49]:
             self.check_over_forward(time_step=__snake_case   )
 def       UpperCamelCase__    (				self   )   ->Optional[Any]:



       '''simple docstring'''





       for t, num_inference_steps in zip([1, 10, 50] ,				[10, 50, 500]   ):
             self.check_over_forward(time_step=__snake_case ,				num_inference_steps=__snake_case   )
 def       UpperCamelCase__    (				self   )   ->List[Any]:



       '''simple docstring'''





       for t, eta in zip([1, 10, 49] ,				[0.0, 0.5, 1.0]   ):
             self.check_over_forward(time_step=__snake_case ,				eta=__snake_case   )
 def       UpperCamelCase__    (				self   )   ->List[Any]:



       '''simple docstring'''





       __lowerCAmelCase   :		Union[str, Any]   =   self.scheduler_classes[0]
       __lowerCAmelCase   :		Union[str, Any]   =   self.get_scheduler_config()
       __lowerCAmelCase   :		Optional[Any]   =   scheduler_class(**__snake_case   )

       assert torch.sum(torch.abs(scheduler._get_variance(0 ,				0   ) - 0.0   )   ) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(420 ,				400   ) - 0.14_771   )   ) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(980 ,				960   ) - 0.32_460   )   ) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(0 ,				0   ) - 0.0   )   ) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(487 ,				486   ) - 0.00_979   )   ) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(999 ,				998   ) - 0.02   )   ) < 1e-5
 def       UpperCamelCase__    (				self   )   ->Tuple:



       '''simple docstring'''





       __lowerCAmelCase   :		Optional[int]   =   self.scheduler_classes[0]
       __lowerCAmelCase   :		Tuple   =   self.get_scheduler_config()
       __lowerCAmelCase   :		str   =   scheduler_class(**__snake_case   )

       __lowerCAmelCase,				__lowerCAmelCase   :		Tuple   =   10, 0.0
       scheduler.set_timesteps(__snake_case   )

       __lowerCAmelCase   :		Optional[Any]   =   self.dummy_model()
       __lowerCAmelCase   :		List[Any]   =   self.dummy_sample_deter
       __lowerCAmelCase   :		Optional[int]   =   self.dummy_sample_deter + 0.1
       __lowerCAmelCase   :		Optional[int]   =   self.dummy_sample_deter - 0.1

       __lowerCAmelCase   :		str   =   samplea.shape[0]
       __lowerCAmelCase   :		Union[str, Any]   =   torch.stack([samplea, samplea, samplea] ,				dim=0   )
       __lowerCAmelCase   :		List[Any]   =   torch.arange(__snake_case   )[0:3, None].repeat(1 ,				__snake_case   )

       __lowerCAmelCase   :		List[str]   =   model(samples.flatten(0 ,				1   ) ,				timesteps.flatten(0 ,				1   )   )
       __lowerCAmelCase   :		int   =   scheduler.batch_step_no_noise(__snake_case ,				timesteps.flatten(0 ,				1   ) ,				samples.flatten(0 ,				1   ) ,				__snake_case   )

       __lowerCAmelCase   :		str   =   torch.sum(torch.abs(__snake_case   )   )
       __lowerCAmelCase   :		Optional[Any]   =   torch.mean(torch.abs(__snake_case   )   )

       assert abs(result_sum.item() - 1_147.7_904   ) < 1e-2
       assert abs(result_mean.item() - 0.4_982   ) < 1e-3
 def       UpperCamelCase__    (				self   )   ->Union[str, Any]:



       '''simple docstring'''





       __lowerCAmelCase   :		List[str]   =   self.full_loop()

       __lowerCAmelCase   :		List[Any]   =   torch.sum(torch.abs(__snake_case   )   )
       __lowerCAmelCase   :		List[str]   =   torch.mean(torch.abs(__snake_case   )   )

       assert abs(result_sum.item() - 172.0_067   ) < 1e-2
       assert abs(result_mean.item() - 0.223_967   ) < 1e-3
 def       UpperCamelCase__    (				self   )   ->str:



       '''simple docstring'''





       __lowerCAmelCase   :		List[Any]   =   self.full_loop(prediction_type='''v_prediction'''   )

       __lowerCAmelCase   :		List[Any]   =   torch.sum(torch.abs(__snake_case   )   )
       __lowerCAmelCase   :		List[Any]   =   torch.mean(torch.abs(__snake_case   )   )

       assert abs(result_sum.item() - 52.5_302   ) < 1e-2
       assert abs(result_mean.item() - 0.0_684   ) < 1e-3
 def       UpperCamelCase__    (				self   )   ->Any:



       '''simple docstring'''





       __lowerCAmelCase   :		Dict   =   self.full_loop(set_alpha_to_one=__snake_case ,				beta_start=0.01   )
       __lowerCAmelCase   :		Tuple   =   torch.sum(torch.abs(__snake_case   )   )
       __lowerCAmelCase   :		str   =   torch.mean(torch.abs(__snake_case   )   )

       assert abs(result_sum.item() - 149.8_295   ) < 1e-2
       assert abs(result_mean.item() - 0.1_951   ) < 1e-3





 def       UpperCamelCase__    (				self   )   ->Union[str, Any]:



       '''simple docstring'''





       __lowerCAmelCase   :		Optional[Any]   =   self.full_loop(set_alpha_to_one=__snake_case ,				beta_start=0.01   )
       __lowerCAmelCase   :		int   =   torch.sum(torch.abs(__snake_case   )   )
       __lowerCAmelCase   :		List[str]   =   torch.mean(torch.abs(__snake_case   )   )

       assert abs(result_sum.item() - 149.0_784   ) < 1e-2
       assert abs(result_mean.item() - 0.1_941   ) < 1e-3






from math import factorial

class     A__   :







    """simple docstring"""

    def __init__(			self     ,   __snake_case     ,   __snake_case       ):
         snake_case											=							real
         if isinstance(__snake_case     ,   __snake_case       ):
              snake_case											=							[1] * rank
         else:
              snake_case											=							rank
    def __repr__(			self       ):
         return (
             F'''{self.real}+'''
             F'''{"+".join(str(__snake_case       )+"E"+str(n+1       )for n,dual in enumerate(self.duals       )       )}'''
         )
    def 						a_     (			self       ):
         snake_case											=							self.duals.copy()
         while cur[-1] == 0:
              cur.pop(-1       )
         return Dual(self.real     ,   __snake_case       )
    def __add__(			self     ,   __snake_case       ):
         if not isinstance(__snake_case     ,   __snake_case       ):
              return Dual(self.real + other     ,   self.duals       )
         snake_case											=							self.duals.copy()
         snake_case											=							other.duals.copy()
         if len(__snake_case       ) > len(__snake_case       ):
              o_dual.extend([1] * (len(__snake_case       ) - len(__snake_case       ))       )
         elif len(__snake_case       ) < len(__snake_case       ):
              s_dual.extend([1] * (len(__snake_case       ) - len(__snake_case       ))       )
         snake_case											=							[]
         for i in range(len(__snake_case       )       ):
              new_duals.append(s_dual[i] + o_dual[i]       )
         return Dual(self.real + other.real     ,   __snake_case       )

    __magic_name__         =						__add__
    def __sub__(			self     ,   __snake_case       ):
         return self + other * -1
    def __mul__(			self     ,   __snake_case       ):
         if not isinstance(__snake_case     ,   __snake_case       ):
              snake_case											=							[]
              for i in self.duals:
                   new_duals.append(i * other       )
              return Dual(self.real * other     ,   __snake_case       )
         snake_case											=							[0] * (len(self.duals       ) + len(other.duals       ) + 1)
         for i, item in enumerate(self.duals       ):
              for j, jtem in enumerate(other.duals       ):
                   new_duals[i + j + 1] += item * jtem
         for k in range(len(self.duals       )       ):
              new_duals[k] += self.duals[k] * other.real
         for index in range(len(other.duals       )       ):
              new_duals[index] += other.duals[index] * self.real
         return Dual(self.real * other.real     ,   __snake_case       )

    __magic_name__         =						__mul__
    def __truediv__(			self     ,   __snake_case       ):
         if not isinstance(__snake_case     ,   __snake_case       ):
              snake_case											=							[]
              for i in self.duals:
                   new_duals.append(i / other       )
              return Dual(self.real / other     ,   __snake_case       )
         raise ValueError
    def __floordiv__(			self     ,   __snake_case       ):
         if not isinstance(__snake_case     ,   __snake_case       ):
              snake_case											=							[]
              for i in self.duals:
                   new_duals.append(i // other       )
              return Dual(self.real // other     ,   __snake_case       )
         raise ValueError




    def __pow__(			self     ,   __snake_case       ):
         if n < 0 or isinstance(__snake_case     ,   __snake_case       ):
              raise ValueError('''power must be a positive integer'''       )
         if n == 0:
              return 1
         if n == 1:
              return self
         snake_case											=							self
         for _ in range(n - 1       ):
              x *= self
         return x






def      UpperCAmelCase__		(UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_							):



     """simple docstring"""






     if not callable(UpperCamelCase_							):
          raise ValueError('''differentiate() requires a function as input for func'''							)
     if not isinstance(UpperCamelCase_				,(float, int)							):
          raise ValueError('''differentiate() requires a float as input for position'''							)
     if not isinstance(UpperCamelCase_				,UpperCamelCase_							):
          raise ValueError('''differentiate() requires an int as input for order'''							)
     snake_case											=							Dual(UpperCamelCase_				,1							)
     snake_case											=							func(UpperCamelCase_							)
     if order == 0:
          return result.real
     return result.duals[order - 1] * factorial(UpperCamelCase_							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 def      UpperCAmelCase__		(UpperCamelCase_							):



      """simple docstring"""






      return y**2 * y**4

 print(differentiate(f, 9, 2))




